Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dandye/ai-runbooks --skill enrich-iocgit clone --depth 1 https://github.com/dandye/ai-runbooksWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dandye/ai-runbooks/enrich-ioc)<a href="https://agentmods.dev/skills/dandye/ai-runbooks/enrich-ioc"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/enrich-ioc/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dandye/ai-runbooks/enrich-ioc"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/enrich-ioc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.00769 |
| Opus 5.5 | $0.00023 | $0.00308 |
| Sonnet 5.5 | $0.00011 | $0.00154 |
| Haiku 4.5 | $0.00006 | $0.00077 |
Grade A, and why
enrich-ioc scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enrich IOC Skill
Perform standardized enrichment for a single Indicator of Compromise (IOC) using Google Threat Intelligence (GTI) and Chronicle SIEM.
Inputs
IOC_VALUE- The indicator value (e.g., "198.51.100.10", "evil-domain.com", "abcdef123456...", "http://bad.url/path")IOC_TYPE- The type: "IP Address", "Domain", "File Hash", or "URL"
Workflow
Step 1: GTI Enrichment
Based on IOC_TYPE, call the appropriate GTI tool:
| IOC Type | Tool | Example |
|---|---|---|
| IP Address | gti-mcp.get_ip_address_report |
get_ip_address_report(ip_address="198.51.100.10") |
| Domain | gti-mcp.get_domain_report |
get_domain_report(domain="evil-domain.com") |
| File Hash | gti-mcp.get_file_report |
get_file_report(hash="abcdef123...") |
| URL | gti-mcp.get_url_report |
get_url_report(url="http://bad.url/path") |
Store key findings in GTI_FINDINGS:
- Reputation score
- Classification (malicious, suspicious, clean)
- Key relationships (contacted domains, IPs, etc.)
- Associated malware families or campaigns
Error Handling: If GTI fails (quota exceeded, IOC not found), note the limitation and proceed with SIEM enrichment.
Step 2: SIEM Entity Lookup
secops-mcp.lookup_entity(entity_value=IOC_VALUE)
Store in SIEM_ENTITY_SUMMARY:
- First/last seen timestamps
- Related alerts
- Associated assets/users
Step 3: SIEM IOC Match Check
secops-mcp.get_ioc_matches()
Check if IOC_VALUE appears in results. Store Yes/No in SIEM_IOC_MATCH_STATUS.
Required Outputs
After completing this skill, you MUST report these outputs:
| Output | Description |
|---|---|
GTI_FINDINGS |
Summary of GTI report (reputation, classification, relationships) |
SIEM_SUMMARY |
SIEM entity context (first/last seen, related alerts) |
IOC_MATCH_STATUS |
Yes/No - whether IOC appears in recent threat feed matches |
THREAT_SCORE |
Numerical threat score (0-100) based on GTI reputation |
MALICIOUS_CONFIDENCE |
Confidence level: high, medium, low, or none |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 86 lines · 57 tokens per session scan A 52581afe2f38
enrich-ioc is a skill published in the GitHub repository dandye/ai-runbooks (132 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 57 tokens to every session and 769 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
enrich-ioc compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| enrich-iocdandye/ai-runbooks | Skill | 132 | 2mo ago | Apache-2.0 | A | 57 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 315k | 5d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |